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J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:p> The integration of renewable energy sources (RES) into new power system (NPS) represents a crucial approach for reducing carbon emissions (CEs) and mitigating resource consumption in cloud computing centers. However, the inherent uncertainties of RES generation, such as randomness, volatility, and intermittency, pose significant challenges to the scheduling of NPS. A data-driven multi-energy complementary uncertainty scheduling method is proposed to address these issues and ensure a stable power supply from NPS to cloud computing centers. First, we constructed a stochastic differential equation (SDE) to represent the uncertainty characteristics of renewable energy generation, and proposed a SDE deep network based on LSTM (LSTM-SDE Net) to capture and learn the aleatoric uncertainty and epistemic uncertainty of units; subsequently, we established the multi-energy complementary uncertain many-objective scheduling model (MuC-UMaOSM) by utilizing the interval parametric estimation, which aims to optimize grid total cost (TC), RES consumption rate (CR), CEs, grid economic benefits (EB) and load balancing (LB); finally, in order to obtain trade-off solutions with improved convergence, diversity, and uncertainty, we designed a two-population cooperative interval many-objective evolutionary algorithm (T-PIMaOEA) to solve the proposed model. 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